Anonymizing Data

Read this article on faker:

blog.districtdatalabs.com/a-practical-guide-to-anonymizing-datasets-with-python-faker

The goal: given a target dataset (for example, a CSV file with multiple columns), produce a new dataset such that for each row in the target, the anonymized dataset does not contain any personally identifying information. The anonymized dataset should have the same amount of data and maintain its analytical value. As shown in the figure below, one possible transformation simply maps original information to fake and therefore anonymous information but maintains the same overall structure.